A stochastic parametrization for deep convection using cellular automata

Abstract

Abstract A cellular automaton (CA) is introduced to the deep convection parametrization of the high‐resolution limited‐area model Aire Limitée Adaptation/Application de la Recherche à l'Opérationnel (ALARO). The self‐organizational characteristics of the CA allow for lateral communication between adjacent numerical weather prediction (NWP) model grid boxes and add additional memory to the deep convection scheme. The CA acts in two horizontal dimensions, with finer grid spacing than the NWP model. It is randomly seeded in regions where convective available potential energy (CAPE) exceeds a threshold value. Both deterministic and probabilistic rules, coupled to the large‐scale wind, are explored to evolve the CA in time. Case studies indicate that the scheme has the potential to organize cells along convective squall lines and enhance advective effects. An ensemble of forecasts using the present CA scheme demonstrated an ensemble spread in the resolved wind field in regions where deep convection is large. Such a spread represents the uncertainty due to subgrid variability of deep convection and could be an interesting addition to an ensemble prediction system.